Differentiation of non-hypervascular non-functional pancreatic neuroendocrine neoplasms from solid pseudopapillary neoplasms using dual-layer spectral detector CT.

BACKGROUND: Dual-layer spectral detector CT (DLCT) represents an advanced and emerging modality in CT imaging, offering multiparametric images that enhance the quantitative assessment of pancreatic diseases. Non-hypervascular non-functional pancreatic neuroendocrine neoplasms (NF-pNENs) and solid pseudopapillary neoplasms (SPNs) frequently exhibit overlapping clinical and imaging features, complicating their differentiation. This study aimed to investigate the valuable quantitative parameters of DLCT in preoperative differentiation between non-hypervascular NF-pNEN and SPN, as well as to analyze their diagnostic performance. METHODS: This retrospective study included 52 patients with pathologically confirmed non-hypervascular NF-pNENs and SPNs who underwent DLCT examination before surgery between June 2019 and September 2025. To differentiate between non-hypervascular NF-pNENs and SPNs, independent relevant clinical-radiological features and quantitative parameters were identified using the Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis and multivariate logistic regression analysis. The diagnostic performances of independent variables were assessed through receiver operating characteristic curves. RESULTS: There were 34 patients with non-hypervascular NF-pNENs (46.7 ± 10.2 years, 19 females) and 18 patients with SPNs (32.8 ± 7.9 years, 14 females). Clinical, radiological features and parameters were evaluated with near-perfect agreements among two radiologists. Age and normalised iodine concentration of lesion in the arterial phase (nICa) were the independent factors for differentiating between non-hypervascular NF-pNENs and SPNs in multivariate logistic regression analysis. The areas under the receiver operating characteristic curves (AUCs) for age, nCTa and nICa in tumour differentiation were 0.855 (95% confidence interval [CI], 0.754-0.956; cutoff, 36 years), 0.797 (95% CI, 0.679-0.916; cutoff, 0.285) and 0.891 (95% CI, 0.806-0.975; cutoff, 0.147), respectively. The combined model (age + nICa) demonstrated the highest performance (AUC = 0.980; 95% CI, 0.952-1.000) when comparing to that of age (P = 0.008), nCTa (P = 0.002) and nICa (P = 0.03), with satisfactory accuracy (92.3%), sensitivity (88.2%) and specificity (100%). CONCLUSIONS: The DLCT parameter nICa combined with age facilitates non-invasive quantitative differentiation between non-hypervascular NF-pNENs and SPNs with satisfactory diagnostic performance. CLINICAL TRIAL NUMBER: Not applicable.